arXiv 2409.094132024-09cs.CLcs.AI

感受质结构与语言涌现之间双向影响的建构性方法

Constructive Approach to Bidirectional Influence between Qualia Structure and Language Emergence

Graduate School of Informatics, Kyoto University（京都大学信息学研究科）；Research Organization of Science and Technology, Ritsumeikan University（立命馆大学科学技术研究机构）；Graduate School of Arts and Sciences, The University of Tokyo（东京大学文理研究生院）；Faculty of Human Science, Waseda University（早稻田大学人类科学学部）；Graduate School of Engineering Science, Osaka University（大阪大学工程科学研究生院）；School of Psychological Sciences, Monash University（莫纳什大学心理科学学院）；Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT)（信息与神经网络中心（CiNet），日本信息通信研究机构（NICT））；ATR Computational Neuroscience Laboratories（ATR计算神经科学实验室）

Tadahiro Taniguchi, Masafumi Oizumi, Noburo Saji, Takato Horii, Naotsugu Tsuchiya

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AI总结：

本文提出感受质结构与语言涌现之间存在双向影响，并借助集体预测编码等框架及神经网络语言模型证据，论证语言不仅创造交流工具，也促成对质性体验的共同理解，为意识、语言与认知科学提供建构性研究方向。

摘要：

本文从视角性论文的角度探讨语言涌现与主观体验的关系结构（称为感受质结构）之间的双向影响，并为二者之间复杂的依赖关系提出一种建构性方法。我们假设，具有分布语义的语言（例如句法—语义结构）的涌现，与由经验塑造的内部表征的协调有关，并且这种协调可能通过相互影响促进形成更具结构性的语言。这一假设的相互依赖关系与人工智能和符号涌现机器人学的最新进展相联系，并在本文中通过集体预测编码等理论框架加以探讨。计算研究表明，基于神经网络的语言模型会形成系统化结构化的内部表征，而多模态语言模型能够在语言与感知信息之间共享表征。这一视角表明，语言涌现不仅是一种创造交流工具的机制，也是一种使人们实现对质性体验的共同理解的机制。本文讨论了这种双向影响在意识研究、语言学和认知科学中的意义，并概述了未来进一步探索语言涌现与感受质结构之间动态关系的建构性研究方向。

英文摘要：

This perspective paper explores the bidirectional influence between language emergence and the relational structure of subjective experiences, termed qualia structure, and lays out a constructive approach to the intricate dependency between the two. We hypothesize that the emergence of languages with distributional semantics (e.g., syntactic-semantic structures) is linked to the coordination of internal representations shaped by experience, potentially facilitating more structured language through reciprocal influence. This hypothesized mutual dependency connects to recent advancements in AI and symbol emergence robotics, and is explored within this paper through theoretical frameworks such as the collective predictive coding. Computational studies show that neural network-based language models form systematically structured internal representations, and multimodal language models can share representations between language and perceptual information. This perspective suggests that language emergence serves not only as a mechanism creating a communication tool but also as a mechanism for allowing people to realize shared understanding of qualitative experiences. The paper discusses the implications of this bidirectional influence in the context of consciousness studies, linguistics, and cognitive science, and outlines future constructive research directions to further explore this dynamic relationship between language emergence and qualia structure.

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